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Combining street imagery and spatial information for measuring socioeconomic status

Suel, Esra; Boulleau, Marthe; Ezzati, Majid; Flaxman, Seth; (2018) Combining street imagery and spatial information for measuring socioeconomic status. In: Proceedings of the 32nd Conference on Neural Information Processing Systems (NeurIPS 2018). (pp. p. 20). Neural Information Processing Systems (NeurIPS): Montréal, Canada. Green open access

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Abstract

Emerging sources of large-scale data, such as remote sensing, street imagery, GPS trajectories, coupled with advances in deep learning methods have the potential for significantly advancing how fast, how frequently, and how locally we can measure urban features and population characteristics to inform and evaluate policies. One such example that attracted increasing attention from the research community is utilizing street level imagery for various measurement tasks in this broader context. We believe incorporating spatial information with Gaussian Processes (GPs) can give us better performance when using street images. To test this hypothesis, we empirically investigated multiple approaches for combining spatial and street image information using neural networks and GPs for predicting income, crowding, and education levels in London, UK. Results demonstrated using GPs only with spatial information (without any inputs from images) gives us a good baseline. Complementary value of street images were demonstrated for the socioeconomic status measures we investigated. Further, our results showed superior performance of GP regression of residuals compared to other methods including feeding spatial information as input directly to neural networks.

Type: Proceedings paper
Title: Combining street imagery and spatial information for measuring socioeconomic status
Event: NIPS 2018 Workshop on Modeling and Decision-Making in the Spatiotemporal Domain
Open access status: An open access version is available from UCL Discovery
Publisher version: https://openreview.net/forum?id=HJl2OqjCY7
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: measurement of socioeconomic status, poverty mapping, population mapping, deep learning, Gaussian processes, spatial statistics
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Centre for Advanced Spatial Analysis
URI: https://discovery.ucl.ac.uk/id/eprint/10183382
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